ML Engineer

• Design, develop, evaluate, benchmark, and implement generative AI models and algorithms, utilizing state-of-the-art techniques such as GPT, VAE, GAN, and reinforcement learning.
• Collaborate with cross-functional teams to define project goals, research requirements, and develop innovative solutions, ensuring alignment with overall business goals.
• Optimize model through experimentation, hyperparameter tuning, and advanced optimization techniques for improved performance, scalability, and efficiency.
• Stay up-to-date on the latest advancements in generative AI, deep learning, and related fields, and incorporate new techniques and methods into the team’s workflow.
• Develop and maintain AI pipelines for data preprocessing, feature extraction, model training, evaluation, and deployment.
• Generate high-quality content using various modalities such as text, images, music, code, etc.
• Conduct experiments and analysis to evaluate the performance and quality of the generated content.
• Manage data ingestion processes, oversee labelling efforts, and monitor data quality to support your own machine-learning model.
• Develop and maintain clear and concise documentation (including technical specifications, user guides, and presentations) of generative AI models, processes, and results to communicate complex AI concepts to both technical and non-technical stakeholders.
• Contribute to the establishment of best practices and standards for generative AI development within the organization.
• Provide technical mentorship and guidance to junior team members.

• Master’s degree in Computer Science, Machine Learning, Data Science, or related fields
• 3+ years of experience in developing machine learning systems using Python, TensorFlow, PyTorch, or similar frameworks.
• Deep knowledge of math, probability, statistics, and algorithms
• Solid understanding of machine learning concepts such as supervised learning, unsupervised learning, deep learning, natural language processing, computer vision, etc.
• Hands-on experience in implementing generative models such as GANs, VAEs, Transformers, etc.
• Experience in generating content using various modalities such as text, images, music, code, etc.
• Experience in deploying machine learning models on cloud platforms such as AWS, Azure, GCP, etc.
• Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch).
• Experience optimizing model hyperparameter tuning for speed and cost
• Experience in building MLOps pipelines using MLOps frameworks like Kubeflow, MLFlow, and DataRobot is desired.
• Experience with Docker and Kubernetes
• Familiarity with Eventbridge and AWS step function for workload orchestration
• Excellent analytical and problem-solving skills, with the ability to think critically and creatively to develop innovative AI solutions.

Cognizant (Nasdaq-100: CTSH) is one of the world’s leading professional services companies, transforming clients’ business, operating and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build and run more innovative and efficient businesses. Headquartered in the U.S., Cognizant is ranked 185 on the Fortune 500 and is consistently listed among the most admired companies in the world. Learn how Cognizant helps clients lead with digital at www.cognizant.com or follow us @Cognizant. 00056306611 ]]>